Package: lmtp 1.4.2

lmtp: Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes.

Authors:Nicholas Williams [aut, cre, cph], Iván Díaz [aut, cph]

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Bug tracker:https://github.com/nt-williams/lmtp/issues

On CRAN:

causal-inferencecensored-datalongitudinal-datamachine-learningmodified-treatment-policynonparametric-statisticsprecision-medicinerobust-statisticsstatisticsstochastic-interventionssurvival-analysistargeted-learning

13 exports 56 stars 3.35 score 30 dependencies 109 scripts 449 downloads

Last updated 12 hours agofrom:ab9b1e4cd5 (on devel). Checks:OK: 7. Indexed: yes.

Exports:create_node_listevent_locfipsilmtp_contrastlmtp_controllmtp_ipwlmtp_sdrlmtp_sublmtp_survivallmtp_tmlestatic_binary_offstatic_binary_ontidy

Dependencies:abindassertthatbackportsbitopscaToolscheckmateclicodetoolscvAUCdata.tabledigestforeachfuturefuture.applygamgenericsglobalsgplotsgtoolsisotoneiteratorsKernSmoothlistenvnnlsorigamiparallellyprogressrR6ROCRSuperLearner